Recommendations on benchmarks for numerical air quality model applications in China – Part 2: Ozone and uncertainty analysis
<p>Ground-level ozone (O<span class="inline-formula"><sub>3</sub></span>) has emerged as a significant air pollutant in China, attracting increasing attention from both the scientific community and policymakers. Chemical transport models (CTMs) serve as crucia...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-04-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/25/4233/2025/acp-25-4233-2025.pdf |
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| Summary: | <p>Ground-level ozone (O<span class="inline-formula"><sub>3</sub></span>) has emerged as a significant air pollutant in China, attracting increasing attention from both the scientific community and policymakers. Chemical transport models (CTMs) serve as crucial tools in addressing O<span class="inline-formula"><sub>3</sub></span> pollution, with frequent applications in predicting O<span class="inline-formula"><sub>3</sub></span> concentrations, identifying source contributions, and formulating effective control strategies. The accuracy and reliability of the simulated O<span class="inline-formula"><sub>3</sub></span> concentrations are typically assessed through model performance evaluation (MPE). However, the wide array of CTMs available, variations in input data, model setups, and other factors result in a broad range of differences between simulated and observed O<span class="inline-formula"><sub>3</sub></span> concentrations, highlighting the necessity of standardized benchmarks in O<span class="inline-formula"><sub>3</sub></span> evaluation.</p>
<p>Building upon our previous work, this study conducted a thorough literature review of CTM applications simulating O<span class="inline-formula"><sub>3</sub></span> in China from 2006 to 2021. A total of 216 relevant articles out of a total of 667 reviewed were identified to extract quantitative MPE results and key model configurations. From our analysis, two sets of benchmark values for six commonly used MPE metrics are proposed for CTM applications in China, categorized into “goal” benchmarks representing optimal model performance and “criteria” benchmarks representing achievable model performance across a majority of studies. It is recommended that the normalized mean bias (NMB) for hourly O<span class="inline-formula"><sub>3</sub></span> and daily 8 h maximum O<span class="inline-formula"><sub>3</sub></span> concentrations should ideally fall within <span class="inline-formula">±</span>15 % and <span class="inline-formula">±</span>10 %, respectively, to meet the goal benchmark. If the criteria benchmarks are to be met, the NMB should be within <span class="inline-formula">±</span>30 % and <span class="inline-formula">±</span>20 %, respectively. Moreover, uncertainties in O<span class="inline-formula"><sub>3</sub></span> predictions due to uncertainties in various model inputs were quantified using the decoupled direct method (DDM) in a commonly used CTM. For the simulation period of June 2021, the total uncertainty of simulated O<span class="inline-formula"><sub>3</sub></span> ranged from 4 to 25 <span class="inline-formula">µ</span>g m<span class="inline-formula"><sup>−3</sup></span>, with anthropogenic volatile organic compound (AVOC) emissions contributing most to the uncertainty regarding O<span class="inline-formula"><sub>3</sub></span> in coastal regions and with O<span class="inline-formula"><sub>3</sub></span> boundary conditions playing a dominant role in the northwestern region. The proposed benchmarks for assessing simulated O<span class="inline-formula"><sub>3</sub></span> concentrations, in conjunction with our previous studies on PM<span class="inline-formula"><sub>2.5</sub></span> and other criteria air pollutants, represent a comprehensive and systematic effort to establish a model performance framework for CTM applications in China. These benchmarks aim to support the growing modeling community in China by offering a robust set of evaluation metrics and establishing a consistent evaluation methodology relative to the body of prior research, thereby helping to establish the credibility and reliability of CTM applications. These statistical benchmarks need to be periodically updated as models advance and as better inputs become available in the future.</p> |
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| ISSN: | 1680-7316 1680-7324 |